Keras vs scikit-learn: What are the differences?
Developers describe Keras as "Deep Learning library for Theano and TensorFlow". Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/. On the other hand, scikit-learn is detailed as "Easy-to-use and general-purpose machine learning in Python". scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Keras and scikit-learn can be primarily classified as "Machine Learning" tools.
"Easy and fast NN prototyping" is the primary reason why developers consider Keras over the competitors, whereas "Scientific computing" was stated as the key factor in picking scikit-learn.
Keras and scikit-learn are both open source tools. Keras with 42.5K GitHub stars and 16.2K forks on GitHub appears to be more popular than scikit-learn with 36K GitHub stars and 17.6K GitHub forks.
Repro, Home61, and MonkeyLearn are some of the popular companies that use scikit-learn, whereas Keras is used by StyleShare Inc., Home61, and Suggestic. scikit-learn has a broader approval, being mentioned in 71 company stacks & 40 developers stacks; compared to Keras, which is listed in 52 company stacks and 50 developer stacks.
For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.
We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.
A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.
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